An efficient modified grey wolf optimizer with Lévy flight for optimization tasks
By Ali Asghar Heidari, Parham Pahlavani. Applied Soft Computing, 2017
432 citations recorded by the source corpus.
Abstract The grey wolf optimizer (GWO) is a new efficient population-based optimizer. The GWO algorithm can reveal an efficient performance compared to other well-established optimizers. However, because of the insufficient diversity of wolves in some cases, a problem of concern is that the GWO can still be prone to stagnation at local optima. In this article, an improved modified GWO algorithm is proposed for solving either global or real-world optimization problems. In order to boost the efficacy of GWO, Levy flight (LF) and greedy selection strategies are integrated with the modified hunting phases. LF is a class of scale-free walks with randomly-oriented steps according to the Levy distribution. In order to investigate the effectiveness of the modified Levy-embedded GWO (LGWO), it was compared with several state-of-the-art optimizers on 29 unconstrained test beds. Furthermore, 30 artificial and 14 real-world problems from CEC2014 and CEC2011 were employed to evaluate the LGWO algorithm. Also, statistical tests were employed to investigate the significance of the results. Experimental results and statistical tests demonstrate that the performance of LGWO is significantly better than GWO and other analyzed optimizers.
Subfields: Artificial Intelligence, Computational Theory and Mathematics, Modeling and Simulation
View Paper: https://www.semanticscholar.org/paper/5cb47945eaff3ddfa1ab17021acce2b43f510174
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In Artificial Intelligence
In Computational Theory and Mathematics
Citations in the corpus
- An efficient binary Salp Swarm Algorithm with crossover scheme for feature selection problems (2018, neutral): “AC CE PT ED M AN US CR IP T The GWO is a successful SI algorithm that mimics the social hierarchy and hunting traits of the grey wolves [21, 43, 44, 45].”
- An improved grey wolf optimizer for solving engineering problems (2021, background): “In contrast, it suffers from the lack of the population diversity, imbalance between the exploitation and exploration, and the premature convergence (Heidari et al., 2017; Lu et al., 2018; Tu et al., 2019a).”